This dataset comprises of two .csv format files used within workstream 2 of the Wellcome Trust funded ‘Orphan drugs: High prices, access to medicines and the transformation of biopharmaceutical innovation’ project (219875/Z/19/Z). They appear in various outputs, e.g. publications and presentations.

The deposited data were gathered using the University of Amsterdam Digital Methods Institute’s ‘Twitter Capture and Analysis Toolset’ (DMI-TCAT) before being processed and extracted from Gephi. DMI-TCAT queries Twitter’s STREAM Application Programming Interface (API) using SQL and retrieves data on a pre-set text query. It then sends the returned data for storage on a MySQL database. The tool allows for output of that data in various formats. This process aligns fully with Twitter’s service user terms and conditions. The query for the deposited dataset gathered a 1% random sample of all public tweets posted between 10-Feb-2021 and 10-Mar-2021 containing the text ‘Rare Diseases’ and/or ‘Rare Disease Day’, storing it on a local MySQL database managed by the University of Sheffield School of Sociological Studies (http://dmi-tcat.shef.ac.uk/analysis/index.php), accessible only via a valid VPN such as FortiClient and through a permitted active directory user profile. The dataset was output from the MySQL database raw as a .gexf format file, suitable for social network analysis (SNA). It was then opened using Gephi (0.9.2) data visualisation software and anonymised/pseudonymised in Gephi as per the ethical approval granted by the University of Sheffield School of Sociological Studies Research Ethics Committee on 02-Jun-201 (reference: 039187). The deposited dataset comprises of two anonymised/pseudonymised social network analysis .csv files extracted from Gephi, one containing node data (Issue-networks as excluded publics – Nodes.csv) and another containing edge data (Issue-networks as excluded publics – Edges.csv). Where participants explicitly provided consent, their original username has been provided. Where they have provided consent on the basis that they not be identifiable, their username has been replaced with an appropriate pseudonym. All other usernames have been anonymised with a randomly generated 16-digit key. The level of anonymity for each Twitter user is provided in column C of deposited file ‘Issue-networks as excluded publics – Nodes.csv’.

This dataset was created and deposited onto the University of Sheffield Online Research Data repository (ORDA) on 26-Aug-2021 by Dr. Matthew S. Hanchard, Research Associate at the University of Sheffield iHuman institute/School of Sociological Studies. ORDA has full permission to store this dataset and to make it open access for public re-use without restriction under a CC BY license, in line with the Wellcome Trust commitment to making all research data Open Access.

The University of Sheffield are the designated data controller for this dataset.

This dataset was generated to train models for research in the Radiation Planning Assistant (RPA), aimed at auto-contouring cervical lymph node levels in the head and neck.

• Patients: Training = 32 (31 unique); Test = 15
• Acquisition Protocol: See accompanying spreadsheet (TCIA_RPA_HN_LNs_Aquisition_Protocols) for more details. All patients were imaged supine with head holder and thermoplastic mask specified in our institution’s simulation protocol for radiotherapy head and neck.
• Scanner Details: Philips/Brilliance Big Bore (89.4%) | SIEMENS/SOMATOM Definition Edge (10.6%)
• Patient Inclusion Criteria: CT scans of 46 unique patients who retrospectively received head and neck radiotherapy at MD Anderson Cancer Center from April 2019 to June 2021 were curated. All data was gathered under an approved institutional review board protocol. These patients received definitive radiotherapy to the oropharynx with no nodal dissection.
• Segmentation Generation: First, five head and neck sub-specialized radiation oncologists manually contoured seven lymph node levels (IA, IB, II, III, IV, V, RP (Retropharyngeal or VIIA)) on 3 patients each, totaling 15 patients. This resulted in a collection of 105 lymph node level contours (5 physicians x 3 patients x 7 contours). Left and right contours for each nodal level were combined into one volume to prevent misclassification from left-right flipping augmentations. Contours were anatomically drawn without margin according to institutional practice. This dataset was comprised of images and contours of definitive Oropharynx Cancer (OPX) patients only and did not contain images and contours of those who had nodal level dissections at the time of radiotherapy. These 15 patients’ contours were used to train an initial model. This initial model was then used to generate lymph node level contours on 32 additional CT scans (31 unique patients), which were subsequently edited and reviewed for quality by a radiation oncology resident to be training data for the final model. The 15-patient cohort was then used as test data for the final model. The contours were split into left and right contours in post-processing.
  • Structures of interest: LN_Neck_IA_L, LN_Neck_IA_R, LN_Neck_IB_L, LN_Neck_IB_R, LN_Neck_II_L, LN_Neck_II_R, LN_Neck_III_L, LN_Neck_III_R, LN_Neck_IV_L, LN_Neck_IV_R, LN_Neck_V_L, LN_Neck_V_R, LN_Neck_RP_L, LN_Neck_RP_R
• Note: In processing the image data, a single patient was accidentally anonymized more than once. The dataset for this model used a duplicate of one of those patients as 2 training subjects. The duplication was discovered after the journal publication. The duplicate anonymized patient IDs are RPA-HN-Lymph-Nodes-006 and RPA-HN-Lymph-Nodes-017. The duplicate was removed from the TCIA dataset; RPA-HN-Lymph-Nodes-006 was removed. Patient ID RPA-HN-Lymph-Nodes-017 may be used twice if the user wishes to duplicate the findings in the journal publication.

This repository contains the datasets used in evaluation of the following paper: "EtherDiffer: Differential Testing on RPC Services of Ethereum Nodes" (ESEC/FSE 2023)Effectiveness of Test Case Generation: RQ1.tar.gz3,000 semantically-valid test cases: valid_tcs/3,000 semantically-invalid test cases: invalid_tcs/Result file: tc_gen.statsDeviation and Bug Detection Capability: RQ2.tar.gzResult from a single iteration: iter-[#]/where each iter-[#]/ contains:Network configuration files for each target node: configs/Chain data directory for each target node: data/Generated test cases: testcases/Execution results from each target node: exec-resultsError and value deviations found by EtherDiffer: reports/Comparison with the Official Tool: RQ3.tar.gzData for chain generation: chain/Data for test case generation: test_case/where chain/ contains:Generated chain from EtherDiffer: data-EtherDiffer.tar.gzGenerated chain from Hive: data-hive.tar.gzScript to extract chain data: chain.jsResult file for EtherDiffer chain: EtherDiffer_chain.statsResult file for Hive chain: hive_chain.statswhere test_case/ containsGenerated test cases from EtherDiffer: EtherDiffer/Generated test cases from Hive: hive/

This data set contains the complete behavioral and neurophysiological data obtained in two behaving monkeys as was analyzed in two publications by Rostami et al. (2024) and Rickert et al. (2009). The experimental task involves a delayed center-out arm-reach in three experimental condition that varied the degree of information (complete or incomplete) about the final movement target as cued at the start of the delay period. The data set contains the trigger events for the experimental cue stimuli and the behavioral events, and it contains acute single-unit recordings from the primary motor cortex (M1) and premotor cortex dorsal (PMd) for both monkeys. All experiments were conducted in the laboratory and under supervision of Dr. Alexa Riehle (Aix-Marseille Université and Research Center Jülich). Please refer to the README file in the data repository for additional information.

This record contains raw data related to article “External Validation and Comparison of Two Nomograms Predicting the Probability of Lymph Node Involvement in Patients subjected to Robot-Assisted Radical Prostatectomy and Concomitant Lymph Node Dissection: A Single Tertiary Center Experience in the MRI-Era"

Abstract

Introduction: To externally validate and directly compare the performance of the Briganti 2012 and Briganti 2019 nomograms as predictors of lymph node invasion (LNI) in a cohort of patients treated with robot-assisted radical prostatectomy (RARP) and extended pelvic lymph node dissection (ePLND).

Materials and methods: After the exclusion of patients with incomplete biopsy, imaging, or clinical data, 752 patients who underwent RARP and ePLND between December 2014 to August 2021 at our center, were included. Among these patients, 327 (43.5%) had undergone multi-parametric MRI (mpMRI) and mpMRI-targeted biopsy. The preoperative risk of LNI was calculated for all patients using the Briganti 2012 nomogram, while the Briganti 2019 nomogram was used only in patients who had performed mpMRI with the combination of targeted and systematic biopsy. The performances of Briganti 2012 and 2019 models were evaluated using the area under the receiver-operating characteristics curve analysis, calibrations plot, and decision curve analysis.

Results: A median of 13 (IQR 9-18) nodes per patient was removed, and 78 (10.4%) patients had LNI at final pathology. The area under the curves (AUCs) for Briganti 2012 and 2019 were 0.84 and 0.82, respectively. The calibration plots showed a good correlation between the predicted probabilities and the observed proportion of LNI for both models, with a slight tendency to underestimation. The decision curve analysis (DCA) of the two models was similar, with a slightly higher net benefit for Briganti 2012 nomogram. In patients receiving both systematic- and targeted-biopsy, the Briganti 2012 accuracy was 0.85, and no significant difference was found between the AUCs of 2012 and 2019 nomograms (p = 0.296). In the sub-cohort of 518 (68.9%) intermediate-risk PCa patients, the Briganti 2012 nomogram outperforms the 2019 model in terms of accuracy (0.82 vs. 0.77), calibration curve, and net benefit at DCA.

Conclusion: The direct comparison of the two nomograms showed that the most updated nomogram, which included MRI and MRI-targeted biopsy data, was not significantly more accurate than the 2012 model in the prediction of LNI, suggesting a negligible role of mpMRI in the current population.

The SmartBay Observatory in Galway Bay is an underwater observatory which uses cameras, probes and sensors to permit continuous and remote live underwater monitoring. It was installed in 2015 on the seafloor 1.5km off the coast of Spiddal, Co. Galway, Ireland at a depth of 20-25m. Underwater observatories allow ocean researchers unique real-time access to monitor ongoing changes in the marine environment. The Galway Bay Observatory is an important contribution by Ireland to the growing global network of real-time data capture systems deployed in the ocean. Data relating to the marine environment at the Galway Observatory site is transferred in real-time through a fibre optic telecommunications cable to the Marine Institute headquarters and then made publically available on the internet. The data includes a live video stream, the depth of the observatory node, the water temperature and salinity, and estimates of the chlorophyll and turbidity levels in the water which give an indication of the volume of phytoplankton and other particles, such as sediment, in the water. Maintenance take place on the observatory every 18 to 24 months. A Teledyne RDI Workhorse Broadband 600KHz acoustic Doppler current profiler (ADCP ) is mounted on the observatory infrastructure. It is approximately two metres above the seabed, looking up towards the sea surface. Data are collected every minute for 1.0m depth bins from approx. 1 metre above the instrument to the surface. Data are collected in RDI's PD0 binary format (more information is available from the link: http://spiddal.marine.ie/data.html#adcp). The .PD0 raw files are processed as they are collected and the real time values for the Near Surface and Mid Water Current Speeds and Directions are extracted. The extracted values are displayed in near-real-time on smartbay.marine.ie. The processed data is also provided via an ERDDAP data server allowing the data to be downloaded in a range of open formats. The sensor is deployed on the EMSO Smartbay Cable End Equipment Node in Galway Bay in approx. 25m depth of water. acknowledgement=The SmartBay Observatory was funded in part by a grant from Science Foundation Ireland under Grant Number 12/RI/2331. Ongoing operation of the observatory is funded by the Marine Institute and Sustainable Energy Authority of Ireland. area=North Atlantic Ocean array=SmartBay cdm_data_type=Point contact=data_requests@marine.ie contributor_name=Alan Berry; Conall O'Malley; Rob Thomas contributor_role=ProjectManager; ProjectMember; DataManager Conventions=Copernicus-InSituTAC-FormatManual-1.4, SeaDataNet_1.0, CF-1.6, OceanSITES-1.3, ACDD-1.2, COARDS data_mode=R data_type=OceanSITES time-series data defaultDataQuery=&time>=now-30minutes defaultGraphQuery=time,bin_number,velocity&time>=now-7days&.marker=2|10 featureType=Point geospatial_lat_max=53.22733 geospatial_lat_min=53.22733 geospatial_lat_units=degrees_north geospatial_lon_max=-9.26629 geospatial_lon_min=-9.26629 geospatial_lon_units=degrees_east geospatial_vertical_max=20 geospatial_vertical_min=20 geospatial_vertical_positive=down geospatial_vertical_units=m history=2020-04-20: Metadata attributes enhanced following OceanSITES and SeaDataNet attribute and vocabulary requirements. 2020-05-13: Metadata attributes enhanced following Copernicus Marine In Situ NetCDF format manual. id=ie.marine.data:dataset.2820 infoUrl=http://data.marine.ie/geonetwork/srv/eng/catalog.search#/metadata/ie.marine.data:dataset.2820 institution=Marine Institute institution_edmo_code=396 institution_references=https://www.marine.ie keywords_vocabulary=SeaDataNet Parameter Discovery Vocabulary licenseURL=https://creativecommons.org/licenses/by/4.0/legalcode naming_authority=Marine Institute principal_investigator=Paul Gaughan principal_investigator_email=paul.gaughan@marine.ie principal_investigator_url=https://www.smartbay.ie/ processing_level=Instrument data that has been converted to geophysical values QC_indicator=unknown source=fixed benthic node source_platform_category_code=11 sourceUrl=(Cassandra) standard_name_vocabulary=CF Standard Name Table v72 subsetVariables=instrument_id, serial_number time_coverage_resolution=PT1M time_coverage_start=2017-05-04T14:34:00Z

email-EuAll: EU email communication network

The network was generated using email data from a large European research institution. For a period from October 2003 to May 2005 (18 months) we have anonymized information about all incoming and outgoing email of the research institution. For each sent or received email message we know the time, the sender and the recipient of the email. Overall we have 3,038,531 emails between 287,755 different email addresses. Note that we have a complete email graph for only 1,258 email addresses that come from the research institution. Furthermore, there are 34,203 email addresses that both sent and received email within the span of our dataset. All other email addresses are either non-existing, mistyped or spam.

Given a set of email messages, each node corresponds to an email address. We create a directed edge between nodes i and j, if i sent at least one message to j.

email-Enron: Enron email network

Enron email communication network covers all the email communication within a dataset of around half million emails. This data was originally made public, and posted to the web, by the Federal Energy Regulatory Commission during its investigation. Nodes of the network are email addresses and if an address i sent at least one email to address j, the graph contains an undirected edge from i to j. Note that non-Enron email addresses act as sinks and sources in the network as we only observe their communication with the Enron email addresses.

The Enron email data was originally released by William Cohen at CMU.

wiki-Talk: Wikipedia Talk network

Wikipedia is a free encyclopedia written collaboratively by volunteers around the world. Each registered user has a talk page, that she and other users can edit in order to communicate and discuss updates to various articles on Wikipedia. Using the latest complete dump of Wikipedia page edit history (from January 3 2008) we extracted all user talk page changes and created a network.

The network contains all the users and discussion from the inception of Wikipedia till January 2008. Nodes in the network represent Wikipedia users and a directed edge from node i to node j represents that user i at least once edited a talk page of user j.

comm-f2f-Resistance: Dynamic Face-to-Face Interaction Networks

The dynamic face-to-face interaction networks represent the interactions that happen during discussions between a group of participants playing the Resistance game. This dataset contains networks extracted from 62 games. Each game is played by 5-8 participants and lasts between 45--60 minutes. We extract dynamically evolving networks from the free-form discussions using the ICAF algorithm. The extracted networks are used to characterize and detect group deceptive behavior using the DeceptionRank algorithm.

The networks are weighted, directed and temporal. Each node represents a participant. At each 1/3 second, a directed edge from node u to v is weighted by the probability of participant u looking at participant v or the laptop. Additionally, we also provide a binary version where an edge from u to v indicates participant u looks at participant v (or the laptop).

Stanford Network Analysis Platform (SNAP) is a general purpose, high performance system for analysis and manipulation of large networks. Graphs consists of nodes and directed/undirected/multiple edges between the graph nodes. Networks are graphs with data on nodes and/or edges of the network.

The core SNAP library is written in C++ and optimized for maximum performance and compact graph representation. It easily scales to massive networks with hundreds of millions of nodes, and billions of edges. It efficiently manipulates large graphs, calculates structural properties, generates regular and random graphs, and supports attributes on nodes and edges. Besides scalability to large graphs, an additional strength of SNAP is that nodes, edges and attributes in a graph or a network can be changed dynamically during the computation.

SNAP was originally developed by Jure Leskovec in the course of his PhD studies. The first release was made available in Nov, 2009. SNAP uses a general purpose STL (Standard Template Library)-like library GLib developed at Jozef Stefan Institute. SNAP and GLib are being actively developed and used in numerous academic and industrial projects.

http://snap.stanford.edu/data/index.html#email

The SmartBay Observatory in Galway Bay is an underwater observatory which uses cameras, probes and sensors to permit continuous and remote live underwater monitoring. The observatory was installed in 2015 on the seafloor 1.5km off the coast of Spiddal, Co. Galway, Ireland at a depth of 20-25m. Underwater observatories allow ocean researchers unique real-time access to monitor ongoing changes in the marine environment. The SmartBay Observatory is an important contribution by Ireland to the growing global network of real-time data capture systems deployed in the ocean. Data relating to the marine environment at the SmartBay Observatory site is transferred in real-time through a fibre optic telecommunications cable to the Marine Institute headquarters and then made publically available on the internet. The data includes a live video stream, the depth of the observatory node, the water temperature and salinity, and estimates of the chlorophyll and turbidity levels in the water which give an indication of the volume of phytoplankton and other particles, such as sediment, in the water. Maintenance take place on the observatory every 18 to 24 months. This CTD (Conductivity, Temperature, Depth) and Oxygen Dataset comprises of the raw data that is collected from the Galway Observatory site using a SeaBird 16plus Conductivity-Temperature-Depth (CTD) sensor probe. The sensor measures the temperature and conductivity of the seawater. The conductivity is used to calculate an estimate of the salinity. The pressure exerted by the seawater above is used to calculate the depth of the sensor, and these parameters are also used to estimate the speed of sound within the sea. The SBE16plus CTD has also been equipped with a SBE43 dissolved oxygen sensor which measures the dissolved oxygen concentration of the seawater. The sensor is deployed on the EMSO Smartbay Cable End Equipment Node in Galway Bay in approx. 25m depth of water. acknowledgement=The SmartBay Observatory was funded in part by a grant from Science Foundation Ireland under Grant Number 12/RI/2331. Ongoing operation of the observatory is funded by the Marine Institute and Sustainable Energy Authority of Ireland. area=North Atlantic Ocean array=SmartBay cdm_data_type=TimeSeries cdm_timeseries_variables=midgid,instrument_id,longitude,latitude,depth contact=data_requests@marine.ie contributor_name=Alan Berry; Conall O'Malley; Rob Thomas contributor_role=ProjectManager; ProjectMember; DataManager Conventions=Copernicus-InSituTAC-FormatManual-1.4, SeaDataNet_1.0, CF-1.6, OceanSITES-1.3, ACDD-1.2, COARDS, NCCSV-1.1 data_mode=R data_type=OceanSITES time-series data defaultDataQuery=&time>=now-60minutes defaultGraphQuery=time,temperature&time>=now-90days&.draw=lines emso_facility=SmartBay featureType=TimeSeries geospatial_lat_max=53.2273 geospatial_lat_min=53.2273 geospatial_lat_units=degrees_north geospatial_lon_max=-9.2663 geospatial_lon_min=-9.2663 geospatial_lon_units=degrees_east geospatial_vertical_max=20 geospatial_vertical_min=20 geospatial_vertical_positive=down geospatial_vertical_units=m ices_area=27.7.b ices_ecoregion=Celtic Seas ices_statistical_rectangle=35E0 id=ie.marine.data:dataset.2839 infoUrl=http://data.marine.ie/geonetwork/srv/eng/catalog.search#/metadata/ie.marine.data:dataset.2839 institution=Marine Institute institution_edmo_code=396 institution_edmo_uri=https://edmo.seadatanet.org/report/396 institution_references=https://www.marine.ie keywords_vocabulary=SeaDataNet Parameter Discovery Vocabulary license_uri=https://spdx.org/licenses/CC-BY-4.0 license_URL=https://creativecommons.org/licenses/by/4.0/legalcode msfd_region=North-east Atlantic Ocean msfd_subregion=Celtic Seas naming_authority=Marine Institute network=EMSO-ERIC platform_name=SmartBay Observatory principal_investigator=Paul Gaughan principal_investigator_email=paul.gaughan@marine.ie principal_investigator_url=https://smartbay.ie processing_level=Instrument data that has been converted to geophysical values seavox_seaarea=ATLANTIC OCEAN > NORTH ATLANTIC OCEAN > NORTHEAST ATLANTIC OCEAN (40W) seavox_urn=SDN:C19::SVX00015 site_code=SmartBay source=fixed benthic node source_platform_category_code=11 sourceUrl=(source database) standard_name_vocabulary=CF Standard Name Table v85 subsetVariables=instrument_id time_coverage_duration=P1Y7M214D time_coverage_end=2023-05-08T13:20:54Z time_coverage_resolution=PT1M time_coverage_start=2021-10-06T06:20:00Z transect=Galway Bay update_interval=PT1M wfd_waterbody_name=Outer Galway Bay wfd_waterbody_type=Coastal

The dataset contains LFP recordings from 8 mice under threat (spider robot predator). These recordings were made using the CBRAIN headstage at a sampling rate of 1024 Hz, capturing data from the medial prefrontal cortex and basolateral amygdala. Behavioral videos and position tracking data are also included.

This derived dataset contains processed neural recordings from the mouse medial prefrontal cortex during tasks assessing sequence replay and representation formation. It includes neural spike data, calcium imaging data, behavioral tracking, and masks for task-specific analyses. The dataset facilitates exploration of neural sequence dynamics, spatiotemporal neural activity, and behavioral correlations. Original raw data can be accessed at https://zenodo.org/records/10528244.

Business process event data modeled as labeled property graphsData Format-----------The dataset comprises one labeled property graph in two different file formats.#1) Neo4j .dump formatA neo4j (https://neo4j.com) database dump that contains the entire graph and can be imported into a fresh neo4j database instance using the following command, see also the neo4j documentation: https://neo4j.com/docs//bin/neo4j-admin.(bat|sh) load --database=graph.db --from=The .dump was created with Neo4j v3.5.#2) .graphml formatA .zip file containing a .graphml file of the entire graphData Schema-----------The graph is a labeled property graph over business process event data. Each graph uses the following concepts:Event nodes - each event node describes a discrete event, i.e., an atomic observation described by attribute "Activity" that occurred at the given "timestamp":Entity nodes - each entity node describes an entity (e.g., an object or a user), it has an EntityType and an identifier (attribute "ID"):Log nodes - describes a collection of events that were recorded together, most graphs only contain one log node:Class nodes - each class node describes a type of observation that has been recorded, e.g., the different types of activities that can be observed, :Class nodes group events into sets of identical observations:CORR relationships - from :Event to :Entity nodes, describes whether an event is correlated to a specific entity; an event can be correlated to multiple entities:DF relationships - "directly-followed by" between two :Event nodes describes which event is directly-followed by which other event; both events in a :DF relationship must be correlated to the same entity node. All :DF relationships form a directed acyclic graph.:HAS relationship - from a :Log to an :Event node, describes which events had been recorded in which event log:OBSERVES relationship - from an :Event to a :Class node, describes to which event class an event belongs, i.e., which activity was observed in the graph:REL relationship - placeholder for any structural relationship between two :Entity nodesThe concepts a further defined in Stefan Esser, Dirk Fahland: Multi-Dimensional Event Data in Graph Databases. CoRR abs/2005.14552 (2020) https://arxiv.org/abs/2005.14552Data Contents-------------neo4j-bpic17-2021-02-17 (.dump|.graphml.zip)An integrated graph describing the raw event data of the entire BPI Challenge 2017 dataset. van Dongen, B.F. (Boudewijn) (2017): BPI Challenge 2017. 4TU.ResearchData. Collection. https://doi.org/10.4121/uuid:5f3067df-f10b-45da-b98b-86ae4c7a310bThis event log pertains to a loan application process of a Dutch financial institute. The data contains all applications filed trough an online system in 2016 and their subsequent events until February 1st 2017, 15:11. The company providing the data and the process under consideration is the same as doi:10.4121/uuid:3926db30-f712-4394-aebc-75976070e91f. However, the system supporting the process has changed in the meantime. In particular, the system now allows for multiple offers per application. These offers can be tracked through their IDs in the log.The data contains the following entities and their events- Application - a credit application document submitted by a customer to a Dutch financial institute- Offer - a loan offer document created by the institute and sent to the customer- Workflow - a logical grouping of activities by the case management system supporting workers at the financial institute to handle applications and offers- Case_R - a user or worker of the financial institute- Case_AO - a derived entity describing the reified relation between an offer and its related application- Case_AW - a derived entity describing the reified relation between the workflow and its related application- Case_WO - a derived entity describing the reified relation between an offer and its related workflowData Size---------BPIC17, nodes: 1425995, relationships: 10300197